Financial markets are fascinating if you can predict them. Also, the traders acting on financial markets produce a vast amount of information to analyse the consequences of investing according to the current market trends. Stock Market prediction is the technique to determine whether stock value will go up or down as it plays an active role in the financial gain of nation's economic status.** We evaluate Nikkei 225 Index prediction models with Modular Neural Network (CNN Layer) and Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the Nikkei 225 Index stock is predictable in the short/long term. **

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold Nikkei 225 Index stock.**

**Nikkei 225 Index, Nikkei 225 Index, stock forecast, machine learning based prediction, risk rating, buy-sell behaviour, stock analysis, target price analysis, options and futures.**

*Keywords:*## Key Points

- How useful are statistical predictions?
- Should I buy stocks now or wait amid such uncertainty?
- Why do we need predictive models?

## Nikkei 225 Index Target Price Prediction Modeling Methodology

The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. We consider Nikkei 225 Index Stock Decision Process with Wilcoxon Rank-Sum Test where A is the set of discrete actions of Nikkei 225 Index stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.^{1,2,3,4}

F(Wilcoxon Rank-Sum Test)

^{5,6,7}= $\begin{array}{cccc}{p}_{\mathrm{a}1}& {p}_{\mathrm{a}2}& \dots & {p}_{1n}\\ & \vdots \\ {p}_{j1}& {p}_{j2}& \dots & {p}_{jn}\\ & \vdots \\ {p}_{k1}& {p}_{k2}& \dots & {p}_{kn}\\ & \vdots \\ {p}_{n1}& {p}_{n2}& \dots & {p}_{nn}\end{array}$ X R(Modular Neural Network (CNN Layer)) X S(n):→ (n+4 weeks) $\begin{array}{l}\int {e}^{x}\mathrm{rx}\end{array}$

n:Time series to forecast

p:Price signals of Nikkei 225 Index stock

j:Nash equilibria

k:Dominated move

a:Best response for target price

For further technical information as per how our model work we invite you to visit the article below:

How do AC Investment Research machine learning (predictive) algorithms actually work?

## Nikkei 225 Index Stock Forecast (Buy or Sell) for (n+4 weeks)

**Sample Set:**Neural Network

**Stock/Index:**Nikkei 225 Index Nikkei 225 Index

**Time series to forecast n: 04 Oct 2022**for (n+4 weeks)

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold Nikkei 225 Index stock.**

**X axis: *Likelihood%** (The higher the percentage value, the more likely the event will occur.)

**Y axis: *Potential Impact%** (The higher the percentage value, the more likely the price will deviate.)

**Z axis (Yellow to Green): *Technical Analysis%**

## Conclusions

Nikkei 225 Index assigned short-term B2 & long-term B2 forecasted stock rating.** We evaluate the prediction models Modular Neural Network (CNN Layer) with Wilcoxon Rank-Sum Test ^{1,2,3,4} and conclude that the Nikkei 225 Index stock is predictable in the short/long term.**

**According to price forecasts for (n+4 weeks) period: The dominant strategy among neural network is to Hold Nikkei 225 Index stock.**

### Financial State Forecast for Nikkei 225 Index Stock Options & Futures

Rating | Short-Term | Long-Term Senior |
---|---|---|

Outlook* | B2 | B2 |

Operational Risk | 30 | 41 |

Market Risk | 66 | 67 |

Technical Analysis | 33 | 58 |

Fundamental Analysis | 87 | 54 |

Risk Unsystematic | 56 | 53 |

### Prediction Confidence Score

## References

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- S. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs, NJ, 3nd edition, 2010
- Clements, M. P. D. F. Hendry (1997), "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, 13, 341–355.
- G. J. Laurent, L. Matignon, and N. L. Fort-Piat. The world of independent learners is not Markovian. Int. J. Know.-Based Intell. Eng. Syst., 15(1):55–64, 2011
- S. Bhatnagar. An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes. Systems & Control Letters, 59(12):760–766, 2010
- A. K. Agogino and K. Tumer. Analyzing and visualizing multiagent rewards in dynamic and stochastic environments. Journal of Autonomous Agents and Multi-Agent Systems, 17(2):320–338, 2008

## Frequently Asked Questions

Q: What is the prediction methodology for Nikkei 225 Index stock?A: Nikkei 225 Index stock prediction methodology: We evaluate the prediction models Modular Neural Network (CNN Layer) and Wilcoxon Rank-Sum Test

Q: Is Nikkei 225 Index stock a buy or sell?

A: The dominant strategy among neural network is to Hold Nikkei 225 Index Stock.

Q: Is Nikkei 225 Index stock a good investment?

A: The consensus rating for Nikkei 225 Index is Hold and assigned short-term B2 & long-term B2 forecasted stock rating.

Q: What is the consensus rating of Nikkei 225 Index stock?

A: The consensus rating for Nikkei 225 Index is Hold.

Q: What is the prediction period for Nikkei 225 Index stock?

A: The prediction period for Nikkei 225 Index is (n+4 weeks)